Chaotic dynamics in terahertz-driven semiconductors with negative effective mass
Bibliographic record
Abstract
We report on a detailed theoretical study of current self-oscillations and chaotic dynamics in negative effective mass (NEM) ${p}^{+}{\mathrm{pp}}^{+}$ diodes driven by dc and ac electric fields with a terahertz (THz) frequency. An ``$N$-shaped'' velocity-field relation is yielded by using the nonparabolic balance-equation theory with a realistic treatment of carrier scatterings by impurity, acoustic phonon, and optic phonon. The dependence of the self-oscillating mode and its frequency on the dc bias, doping concentration, and lattice temperature is examined in detail. The THz-driven ${p}^{+}{\mathrm{pp}}^{+}$ NEM diodes can produce a cooperative nonlinear oscillatory mode which leads to very complicated chaotic dynamics with the dc bias, ac amplitude, and ac frequency as the controlling parameters. The transitions between the periodic and chaotic states are carefully studied by different chaos-detecting methods, such as phase portrait, Poincar\'e bifurcation diagram, power spectrum, and first return map. The resulting power spectrum bifurcation diagram displays a very complicated mosaic structure with a self-similar emergence of high-order mixing frequencies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".